During learning, neurons in the cerebral cortex form new synaptic connections that convey novel information about the new experience. In order for these new connections to have a meaningful impact on neuronal function and animal behavior, they must somehow provide strong enough input to drive action potential firing, the fundamental output of neurons, in the target cell. Achieving this efficiently (i.e. without massive rewiring of the brain) is likely critical for the fast, flexible, and effective learning seen in mammals. Some lines of evidence suggest that an efficient solution to this problem lies in how the inputs are spatially arranged: by clustering the learning-related inputs onto dendrites of the target neuron, the inputs can have a greater impact on action potential firing. While a large body of work has demonstrated that such supra-linear synaptic integration is possible when synapses are close to one another, whether such an arrangement actually arises as a result of learning has been controversial. Further, whether such an arrangement actually drives action potential firing in a way that is relevant to learning is unclear. The research proposed here will use cutting edge imaging techniques to measure the activity of synapses on neurons of the cortex in awake animals while they learn, allowing the determination of whether such clustered activity exists, and how it evolves over learning to impact neuronal firing as well as the execution of a learned behavior. Further, this research will make use of specific genetic tools to gain clues about how such clustering arises during learning. This work will thus help to characterize a basic strategy employed by the brain to efficiently encode information during learning, as well as the mechanisms that allow such a phenomenon to arise.